Multi UAV Trajectory Planning Based on Improved Grey Wolf Algorithm

In the face of increasingly complex task environments and application fields, it is particularly important for multiple drones to make more reasonable trajectory planning in complex environments. Various optimization algorithms have provided great assistance for UAV trajectory planning, among which the grey wolf optimization algorithm has the advantage of simple structure and low implementation difficulty, but also has the disadvantages of poor adaptability, slow convergence speed, and easy to fall into local optima. Therefore, this article intends to improve the grey wolf optimization algorithm, optimize its population initialization method, convergence factor, and individual update strategy, and simulate multiple drone trajectory planning scenarios to verify the feasibility of the improved optimization algorithm. Finally, the simulation results show that this algorithm improves convergence accuracy and speed compared to other algorithms, has better stability and robustness, and has good application value in multiple drone trajectory planning scenarios.